Overview

Dataset statistics

Number of variables12
Number of observations1143
Missing cells0
Missing cells (%)0.0%
Duplicate rows114
Duplicate rows (%)10.0%
Total size in memory107.3 KiB
Average record size in memory96.1 B

Variable types

Numeric12

Alerts

Dataset has 114 (10.0%) duplicate rowsDuplicates
fixed acidity is highly correlated with citric acid and 2 other fieldsHigh correlation
volatile acidity is highly correlated with citric acidHigh correlation
citric acid is highly correlated with fixed acidity and 2 other fieldsHigh correlation
free sulfur dioxide is highly correlated with total sulfur dioxideHigh correlation
total sulfur dioxide is highly correlated with free sulfur dioxideHigh correlation
density is highly correlated with fixed acidityHigh correlation
pH is highly correlated with fixed acidity and 1 other fieldsHigh correlation
fixed acidity is highly correlated with citric acid and 2 other fieldsHigh correlation
volatile acidity is highly correlated with citric acidHigh correlation
citric acid is highly correlated with fixed acidity and 2 other fieldsHigh correlation
free sulfur dioxide is highly correlated with total sulfur dioxideHigh correlation
total sulfur dioxide is highly correlated with free sulfur dioxideHigh correlation
density is highly correlated with fixed acidityHigh correlation
pH is highly correlated with fixed acidity and 1 other fieldsHigh correlation
fixed acidity is highly correlated with pHHigh correlation
free sulfur dioxide is highly correlated with total sulfur dioxideHigh correlation
total sulfur dioxide is highly correlated with free sulfur dioxideHigh correlation
pH is highly correlated with fixed acidityHigh correlation
fixed acidity is highly correlated with citric acid and 3 other fieldsHigh correlation
citric acid is highly correlated with fixed acidity and 3 other fieldsHigh correlation
residual sugar is highly correlated with free sulfur dioxide and 1 other fieldsHigh correlation
chlorides is highly correlated with citric acid and 1 other fieldsHigh correlation
free sulfur dioxide is highly correlated with residual sugar and 1 other fieldsHigh correlation
total sulfur dioxide is highly correlated with free sulfur dioxideHigh correlation
density is highly correlated with fixed acidity and 3 other fieldsHigh correlation
pH is highly correlated with fixed acidity and 3 other fieldsHigh correlation
sulphates is highly correlated with citric acid and 1 other fieldsHigh correlation
alcohol is highly correlated with fixed acidity and 2 other fieldsHigh correlation
citric acid has 99 (8.7%) zeros Zeros

Reproduction

Analysis started2022-02-20 13:30:08.383896
Analysis finished2022-02-20 13:30:29.864459
Duration21.48 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

fixed acidity
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct91
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.311111111
Minimum4.6
Maximum15.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.1 KiB
2022-02-20T14:30:30.510732image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

volatile acidity
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct135
Distinct (%)11.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5313385827
Minimum0.12
Maximum1.58
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.1 KiB
2022-02-20T14:30:30.753340image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.12
5-th percentile0.271
Q10.3925
median0.52
Q30.64
95-th percentile0.84
Maximum1.58
Range1.46
Interquartile range (IQR)0.2475

Descriptive statistics

Standard deviation0.179633193
Coefficient of variation (CV)0.338076697
Kurtosis1.375531299
Mean0.5313385827
Median Absolute Deviation (MAD)0.12
Skewness0.6815474144
Sum607.32
Variance0.03226808404
MonotonicityNot monotonic
2022-02-20T14:30:30.873600image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.632
 
2.8%
0.532
 
2.8%
0.4331
 
2.7%
0.3929
 
2.5%
0.5828
 
2.4%
0.3826
 
2.3%
0.3626
 
2.3%
0.5225
 
2.2%
0.5925
 
2.2%
0.4925
 
2.2%
Other values (125)864
75.6%
ValueCountFrequency (%)
0.122
 
0.2%
0.161
 
0.1%
0.188
0.7%
0.191
 
0.1%
0.22
 
0.2%
0.214
 
0.3%
0.225
0.4%
0.232
 
0.2%
0.2412
1.0%
0.254
 
0.3%
ValueCountFrequency (%)
1.581
 
0.1%
1.332
0.2%
1.181
 
0.1%
1.091
 
0.1%
1.071
 
0.1%
1.043
0.3%
1.0351
 
0.1%
1.0251
 
0.1%
1.023
0.3%
1.0051
 
0.1%

citric acid
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct77
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2683639545
Minimum0
Maximum1
Zeros99
Zeros (%)8.7%
Negative0
Negative (%)0.0%
Memory size9.1 KiB
2022-02-20T14:30:31.008777image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.09
median0.25
Q30.42
95-th percentile0.619
Maximum1
Range1
Interquartile range (IQR)0.33

Descriptive statistics

Standard deviation0.1966858523
Coefficient of variation (CV)0.7329071175
Kurtosis-0.7146856044
Mean0.2683639545
Median Absolute Deviation (MAD)0.17
Skewness0.3715607834
Sum306.74
Variance0.03868532451
MonotonicityNot monotonic
2022-02-20T14:30:31.133115image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
099
 
8.7%
0.4947
 
4.1%
0.2442
 
3.7%
0.0235
 
3.1%
0.0126
 
2.3%
0.2626
 
2.3%
0.3124
 
2.1%
0.0323
 
2.0%
0.2123
 
2.0%
0.0822
 
1.9%
Other values (67)776
67.9%
ValueCountFrequency (%)
099
8.7%
0.0126
 
2.3%
0.0235
 
3.1%
0.0323
 
2.0%
0.0419
 
1.7%
0.0516
 
1.4%
0.0617
 
1.5%
0.0719
 
1.7%
0.0822
 
1.9%
0.0919
 
1.7%
ValueCountFrequency (%)
11
 
0.1%
0.791
 
0.1%
0.763
 
0.3%
0.751
 
0.1%
0.744
0.3%
0.733
 
0.3%
0.721
 
0.1%
0.693
 
0.3%
0.688
0.7%
0.672
 
0.2%

residual sugar
Real number (ℝ≥0)

HIGH CORRELATION

Distinct80
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.532152231
Minimum0.9
Maximum15.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.1 KiB
2022-02-20T14:30:31.256926image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.9
5-th percentile1.6
Q11.9
median2.2
Q32.6
95-th percentile5.195
Maximum15.5
Range14.6
Interquartile range (IQR)0.7

Descriptive statistics

Standard deviation1.355917467
Coefficient of variation (CV)0.535480233
Kurtosis27.67536589
Mean2.532152231
Median Absolute Deviation (MAD)0.3
Skewness4.361096404
Sum2894.25
Variance1.838512176
MonotonicityNot monotonic
2022-02-20T14:30:31.382505image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2107
 
9.4%
2.1103
 
9.0%
1.892
 
8.0%
2.288
 
7.7%
1.980
 
7.0%
2.375
 
6.6%
2.464
 
5.6%
2.661
 
5.3%
1.759
 
5.2%
2.557
 
5.0%
Other values (70)357
31.2%
ValueCountFrequency (%)
0.91
 
0.1%
1.24
 
0.3%
1.35
 
0.4%
1.425
 
2.2%
1.520
 
1.7%
1.642
3.7%
1.652
 
0.2%
1.759
5.2%
1.752
 
0.2%
1.892
8.0%
ValueCountFrequency (%)
15.51
 
0.1%
15.41
 
0.1%
13.82
0.2%
112
0.2%
91
 
0.1%
8.81
 
0.1%
8.61
 
0.1%
8.33
0.3%
8.11
 
0.1%
7.93
0.3%

chlorides
Real number (ℝ≥0)

HIGH CORRELATION

Distinct131
Distinct (%)11.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.08693263342
Minimum0.012
Maximum0.611
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.1 KiB
2022-02-20T14:30:31.525685image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.012
5-th percentile0.054
Q10.07
median0.079
Q30.09
95-th percentile0.123
Maximum0.611
Range0.599
Interquartile range (IQR)0.02

Descriptive statistics

Standard deviation0.04726733795
Coefficient of variation (CV)0.5437237559
Kurtosis47.07832427
Mean0.08693263342
Median Absolute Deviation (MAD)0.01
Skewness6.026360154
Sum99.364
Variance0.002234201237
MonotonicityNot monotonic
2022-02-20T14:30:31.662855image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0848
 
4.2%
0.07741
 
3.6%
0.07438
 
3.3%
0.08438
 
3.3%
0.07836
 
3.1%
0.08235
 
3.1%
0.07534
 
3.0%
0.07633
 
2.9%
0.07931
 
2.7%
0.0729
 
2.5%
Other values (121)780
68.2%
ValueCountFrequency (%)
0.0122
0.2%
0.0341
 
0.1%
0.0382
0.2%
0.0393
0.3%
0.0414
0.3%
0.0422
0.2%
0.0431
 
0.1%
0.0444
0.3%
0.0453
0.3%
0.0462
0.2%
ValueCountFrequency (%)
0.6111
 
0.1%
0.611
 
0.1%
0.4671
 
0.1%
0.4221
 
0.1%
0.4153
0.3%
0.4142
0.2%
0.4031
 
0.1%
0.3871
 
0.1%
0.3581
 
0.1%
0.3411
 
0.1%

free sulfur dioxide
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct53
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.61548556
Minimum1
Maximum68
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.1 KiB
2022-02-20T14:30:31.812032image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q17
median13
Q321
95-th percentile35
Maximum68
Range67
Interquartile range (IQR)14

Descriptive statistics

Standard deviation10.25048612
Coefficient of variation (CV)0.6564308283
Kurtosis1.932169927
Mean15.61548556
Median Absolute Deviation (MAD)6
Skewness1.231261157
Sum17848.5
Variance105.0724658
MonotonicityNot monotonic
2022-02-20T14:30:31.952671image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
699
 
8.7%
580
 
7.0%
1258
 
5.1%
1052
 
4.5%
1551
 
4.5%
751
 
4.5%
948
 
4.2%
1647
 
4.1%
845
 
3.9%
1740
 
3.5%
Other values (43)572
50.0%
ValueCountFrequency (%)
13
 
0.3%
333
 
2.9%
431
 
2.7%
580
7.0%
699
8.7%
751
4.5%
845
3.9%
948
4.2%
1052
4.5%
1139
 
3.4%
ValueCountFrequency (%)
682
0.2%
661
 
0.1%
551
 
0.1%
531
 
0.1%
522
0.2%
512
0.2%
484
0.3%
461
 
0.1%
452
0.2%
432
0.2%

total sulfur dioxide
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct138
Distinct (%)12.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.91469816
Minimum6
Maximum289
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.1 KiB
2022-02-20T14:30:32.109880image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile12
Q121
median37
Q361
95-th percentile112
Maximum289
Range283
Interquartile range (IQR)40

Descriptive statistics

Standard deviation32.78213031
Coefficient of variation (CV)0.7139790006
Kurtosis5.098747772
Mean45.91469816
Median Absolute Deviation (MAD)18
Skewness1.665766014
Sum52480.5
Variance1074.668067
MonotonicityNot monotonic
2022-02-20T14:30:32.251807image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2836
 
3.1%
1528
 
2.4%
1427
 
2.4%
2027
 
2.4%
1826
 
2.3%
2425
 
2.2%
3124
 
2.1%
1624
 
2.1%
1924
 
2.1%
2322
 
1.9%
Other values (128)880
77.0%
ValueCountFrequency (%)
61
 
0.1%
72
 
0.2%
810
 
0.9%
913
1.1%
1017
1.5%
1113
1.1%
1221
1.8%
1319
1.7%
1427
2.4%
1528
2.4%
ValueCountFrequency (%)
2891
0.1%
2781
0.1%
1651
0.1%
1521
0.1%
1511
0.1%
1491
0.1%
1481
0.1%
1472
0.2%
1451
0.1%
1441
0.1%

density
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct388
Distinct (%)33.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.9967304112
Minimum0.99007
Maximum1.00369
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.1 KiB
2022-02-20T14:30:32.417317image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.99007
5-th percentile0.993602
Q10.99557
median0.99668
Q30.997845
95-th percentile1
Maximum1.00369
Range0.01362
Interquartile range (IQR)0.002275

Descriptive statistics

Standard deviation0.00192506713
Coefficient of variation (CV)0.001931381955
Kurtosis0.8881232999
Mean0.9967304112
Median Absolute Deviation (MAD)0.00114
Skewness0.1023951087
Sum1139.26286
Variance3.705883456 × 10-6
MonotonicityNot monotonic
2022-02-20T14:30:32.593751image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.997627
 
2.4%
0.997225
 
2.2%
0.996822
 
1.9%
0.999422
 
1.9%
0.996421
 
1.8%
0.998220
 
1.7%
0.99820
 
1.7%
0.996218
 
1.6%
0.997818
 
1.6%
0.998816
 
1.4%
Other values (378)934
81.7%
ValueCountFrequency (%)
0.990071
0.1%
0.99021
0.1%
0.990642
0.2%
0.990841
0.1%
0.99121
0.1%
0.991541
0.1%
0.991571
0.1%
0.99162
0.2%
0.991621
0.1%
0.99171
0.1%
ValueCountFrequency (%)
1.003691
0.1%
1.00321
0.1%
1.003152
0.2%
1.002891
0.1%
1.00262
0.2%
1.002422
0.2%
1.00222
0.2%
1.00212
0.2%
1.00181
0.1%
1.00152
0.2%

pH
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct87
Distinct (%)7.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.311014873
Minimum2.74
Maximum4.01
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.1 KiB
2022-02-20T14:30:32.746429image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2.74
5-th percentile3.07
Q13.205
median3.31
Q33.4
95-th percentile3.57
Maximum4.01
Range1.27
Interquartile range (IQR)0.195

Descriptive statistics

Standard deviation0.1566640598
Coefficient of variation (CV)0.04731602417
Kurtosis0.9257908123
Mean3.311014873
Median Absolute Deviation (MAD)0.1
Skewness0.2211383921
Sum3784.49
Variance0.02454362762
MonotonicityNot monotonic
2022-02-20T14:30:32.894555image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.341
 
3.6%
3.3640
 
3.5%
3.3838
 
3.3%
3.3937
 
3.2%
3.2633
 
2.9%
3.2832
 
2.8%
3.3232
 
2.8%
3.231
 
2.7%
3.3530
 
2.6%
3.3430
 
2.6%
Other values (77)799
69.9%
ValueCountFrequency (%)
2.741
 
0.1%
2.861
 
0.1%
2.882
 
0.2%
2.893
0.3%
2.91
 
0.1%
2.923
0.3%
2.932
 
0.2%
2.943
0.3%
2.951
 
0.1%
2.985
0.4%
ValueCountFrequency (%)
4.012
0.2%
3.92
0.2%
3.782
0.2%
3.751
 
0.1%
3.741
 
0.1%
3.722
0.2%
3.711
 
0.1%
3.71
 
0.1%
3.693
0.3%
3.684
0.3%

sulphates
Real number (ℝ≥0)

HIGH CORRELATION

Distinct89
Distinct (%)7.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6577077865
Minimum0.33
Maximum2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.1 KiB
2022-02-20T14:30:33.037949image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.33
5-th percentile0.47
Q10.55
median0.62
Q30.73
95-th percentile0.93
Maximum2
Range1.67
Interquartile range (IQR)0.18

Descriptive statistics

Standard deviation0.1703987145
Coefficient of variation (CV)0.2590796672
Kurtosis12.01737703
Mean0.6577077865
Median Absolute Deviation (MAD)0.08
Skewness2.497266051
Sum751.76
Variance0.02903572189
MonotonicityNot monotonic
2022-02-20T14:30:33.182943image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.653
 
4.6%
0.6250
 
4.4%
0.5647
 
4.1%
0.5446
 
4.0%
0.5742
 
3.7%
0.5841
 
3.6%
0.5540
 
3.5%
0.5939
 
3.4%
0.6136
 
3.1%
0.5336
 
3.1%
Other values (79)713
62.4%
ValueCountFrequency (%)
0.331
 
0.1%
0.395
 
0.4%
0.44
 
0.3%
0.424
 
0.3%
0.435
 
0.4%
0.4413
1.1%
0.455
 
0.4%
0.4611
1.0%
0.4713
1.1%
0.4820
1.7%
ValueCountFrequency (%)
21
0.1%
1.952
0.2%
1.621
0.1%
1.611
0.1%
1.561
0.1%
1.362
0.2%
1.341
0.1%
1.331
0.1%
1.311
0.1%
1.261
0.1%

alcohol
Real number (ℝ≥0)

HIGH CORRELATION

Distinct61
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.4421114
Minimum8.4
Maximum14.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.1 KiB
2022-02-20T14:30:33.328833image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum8.4
5-th percentile9.2
Q19.5
median10.2
Q311.1
95-th percentile12.5
Maximum14.9
Range6.5
Interquartile range (IQR)1.6

Descriptive statistics

Standard deviation1.08219561
Coefficient of variation (CV)0.1036376235
Kurtosis0.2211789685
Mean10.4421114
Median Absolute Deviation (MAD)0.7
Skewness0.8633132317
Sum11935.33333
Variance1.171147338
MonotonicityNot monotonic
2022-02-20T14:30:33.460083image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.592
 
8.0%
9.472
 
6.3%
9.857
 
5.0%
9.250
 
4.4%
1049
 
4.3%
10.548
 
4.2%
9.344
 
3.8%
9.644
 
3.8%
9.742
 
3.7%
1139
 
3.4%
Other values (51)606
53.0%
ValueCountFrequency (%)
8.42
 
0.2%
8.51
 
0.1%
8.71
 
0.1%
8.82
 
0.2%
919
 
1.7%
9.117
 
1.5%
9.250
4.4%
9.2333333331
 
0.1%
9.251
 
0.1%
9.344
3.8%
ValueCountFrequency (%)
14.91
 
0.1%
146
0.5%
13.64
0.3%
13.566666671
 
0.1%
13.42
 
0.2%
13.33
0.3%
13.21
 
0.1%
13.12
 
0.2%
133
0.3%
12.97
0.6%

quality
Real number (ℝ≥0)

Distinct6
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.65704287
Minimum3
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.1 KiB
2022-02-20T14:30:33.574839image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile5
Q15
median6
Q36
95-th percentile7
Maximum8
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.8058242481
Coefficient of variation (CV)0.1424461979
Kurtosis0.3146639386
Mean5.65704287
Median Absolute Deviation (MAD)1
Skewness0.2867917005
Sum6466
Variance0.6493527188
MonotonicityNot monotonic
2022-02-20T14:30:33.669767image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
5483
42.3%
6462
40.4%
7143
 
12.5%
433
 
2.9%
816
 
1.4%
36
 
0.5%
ValueCountFrequency (%)
36
 
0.5%
433
 
2.9%
5483
42.3%
6462
40.4%
7143
 
12.5%
816
 
1.4%
ValueCountFrequency (%)
816
 
1.4%
7143
 
12.5%
6462
40.4%
5483
42.3%
433
 
2.9%
36
 
0.5%

Interactions

2022-02-20T14:30:27.955377image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:08.947056image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:10.458886image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:12.113415image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:13.843435image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:15.608409image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:17.360984image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:19.468849image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:21.032652image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:22.769311image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:24.535722image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:26.382496image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:28.074733image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:09.049305image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:10.605174image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:12.250987image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:13.976910image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:15.734693image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:17.489614image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:19.593642image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:21.159749image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:22.925225image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:24.700401image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:26.517048image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:28.200731image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:09.160919image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:10.743819image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:12.396661image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:14.123786image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:15.888577image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:18.196602image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:19.722318image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:21.299368image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:23.086977image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:24.851110image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:26.667462image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:28.325873image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:09.269662image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:10.876435image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:12.534595image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:14.256427image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:16.048679image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:18.334707image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:19.840684image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:21.456626image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:23.204267image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:24.987071image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:26.787385image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:28.451388image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:09.382256image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:11.015710image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:12.673579image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:14.400697image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:16.208602image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:18.465990image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:19.969423image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:21.623009image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:23.333065image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:25.150486image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:26.931702image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:28.573491image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:09.490242image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:11.149482image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:12.885194image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:14.570067image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:16.351419image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:18.591011image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:20.118680image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:21.774912image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:23.454397image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:25.365191image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:27.072910image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:28.700997image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:09.598470image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:11.281772image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:13.039799image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:14.729520image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:16.500794image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:18.706036image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:20.257618image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:21.939852image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:23.595933image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:25.568949image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:27.199776image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:28.827343image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:09.717638image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:11.415855image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:13.165611image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:14.875880image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:16.650484image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:18.816784image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:20.386310image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:22.072578image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:23.757139image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:25.723864image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:27.324491image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:28.966516image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:09.846810image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:11.555610image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:13.306370image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:15.033878image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:16.787882image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:18.947919image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:20.518752image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:22.207528image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:23.932689image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:25.854155image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:27.456083image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:29.099244image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:09.973808image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:11.687683image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:13.438573image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:15.172457image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:16.946302image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:19.085231image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:20.644123image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:22.336837image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:24.081363image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:25.983818image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:27.580165image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:29.222256image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:10.169161image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:11.827047image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:13.575183image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:15.318924image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:17.082339image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:19.221522image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:20.772089image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:22.491730image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:24.237932image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:26.113143image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:27.705513image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:29.347167image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:10.312386image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:11.959628image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:13.712060image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:15.468314image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:17.214404image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:19.348109image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:20.908523image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:22.643865image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:24.392448image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:26.246982image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:27.835582image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-02-20T14:30:33.772542image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:33.941424image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:34.106450image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-20T14:30:34.269573image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Missing values

2022-02-20T14:30:29.548339image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-02-20T14:30:29.781205image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

fixed acidityvolatile aciditycitric acidresidual sugarchloridesfree sulfur dioxidetotal sulfur dioxidedensitypHsulphatesalcoholquality
07.40.700.001.90.07611.034.00.99783.510.569.45
17.80.880.002.60.09825.067.00.99683.200.689.85
27.80.760.042.30.09215.054.00.99703.260.659.85
311.20.280.561.90.07517.060.00.99803.160.589.86
47.40.700.001.90.07611.034.00.99783.510.569.45
57.40.660.001.80.07513.040.00.99783.510.569.45
67.90.600.061.60.06915.059.00.99643.300.469.45
77.30.650.001.20.06515.021.00.99463.390.4710.07
87.80.580.022.00.0739.018.00.99683.360.579.57
96.70.580.081.80.09715.065.00.99593.280.549.25

Last rows

fixed acidityvolatile aciditycitric acidresidual sugarchloridesfree sulfur dioxidetotal sulfur dioxidedensitypHsulphatesalcoholquality
11336.70.3200.442.40.06124.034.00.994843.290.8011.67
11347.50.3100.412.40.06534.060.00.994923.340.8511.46
11355.80.6100.111.80.06618.028.00.994833.550.6610.96
11366.30.5500.151.80.07726.035.00.993143.320.8211.66
11375.40.7400.091.70.08916.026.00.994023.670.5611.66
11386.30.5100.132.30.07629.040.00.995743.420.7511.06
11396.80.6200.081.90.06828.038.00.996513.420.829.56
11406.20.6000.082.00.09032.044.00.994903.450.5810.55
11415.90.5500.102.20.06239.051.00.995123.520.7611.26
11425.90.6450.122.00.07532.044.00.995473.570.7110.25

Duplicate rows

Most frequently occurring

fixed acidityvolatile aciditycitric acidresidual sugarchloridesfree sulfur dioxidetotal sulfur dioxidedensitypHsulphatesalcoholquality# duplicates
96.70.4600.241.70.07718.034.00.994803.390.6010.664
377.50.5100.021.70.08413.031.00.995383.360.5410.564
16.00.5000.001.40.05715.026.00.994483.360.459.553
167.00.6900.072.50.09115.021.00.995723.380.6011.363
237.20.3600.462.10.07424.044.00.995343.400.8511.073
277.20.6950.132.00.07612.020.00.995463.290.5410.153
507.80.6000.262.00.08031.0131.00.996223.210.529.953
839.30.3600.391.50.08041.055.00.996523.470.7310.963
879.90.5400.452.30.07116.040.00.999103.390.629.453
05.20.3400.001.80.05027.063.00.991603.680.7914.062